无人机协助边缘计算的能量收集MEC系统资源分配策略

Resource allocation strategy for UAV-assisted edge computing in energy harvesting MEC system

针对用户请求的计算任务超出地面基站边缘计算服务器计算能力的问题,提出一种无人机(Unmanned Aerial Vehicle,UAV)协助边缘计算的最小化系统能量消耗的资源分配策略。通过引入一个搭载边缘服务器的UAV,当用户请求的计算任务超出地面基站边缘服务器计算能力时,用户将超出的计算任务卸载至协助计算的UAV。在满足能量因果性、计算资源和发射功率的约束条件下,分析了用户能量收集及任务卸载模型,建立了最小化系统能量消耗的资源分配问题。采用传统遗传算法与非线性规划结合的方法,求解建立的非线性规划问题,以获得最优解。仿真结果表明,与其他基准方法相比,所提方法在降低系统能量消耗方面效果更佳。

Aiming at the problem that the computing tasks requested by user equipment exceed the capacity of edge computing servers in the ground base station(BS),an unmanned aerial vehicle(UAV)-assisted resource allocation strategy is proposed in this paper.As a UAV that carries an edge server is deployed,the user equipment can transfer extra computing tasks to the UAV when the tasks requested by the user equipment are beyond the capacity of the ground BS edge server.Under the constraints of the energy causality,computing resources,and the transmitting power,the energy harvesting model and the task offloading model are designed and the resource allocation problem is formulated to minimize system energy consumption.The genetic algorithm(GA)and the nonlinear programming method are combined to obtain the optimal solution of the formulated nonlinear programming problem.Simulation results demonstrate that our approach performs better in reducing system energy consumption compared with other benchmarking methods.